{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification with Claude: Insurance Support Ticket Classifier\n",
    "\n",
    "In this guide, you'll build a high-accuracy classification system that categorizes insurance support tickets into 10 categories. You'll learn how to progressively improve classification accuracy from 70% to 95%+ by combining prompt engineering, retrieval-augmented generation (RAG), and chain-of-thought reasoning.\n",
    "\n",
    "By the end of this guide, you'll understand how to design classification systems that handle complex business rules, work with limited training data, and provide explainable results.\n",
    "\n",
    "## Prerequisites\n",
    "- Python 3.11+ with basic familiarity\n",
    "- Anthropic API key ([get one here](https://console.anthropic.com))\n",
    "- VoyageAI API key (optional - embeddings are pre-computed)\n",
    "- Understanding of classification problems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "First, we will install the required packages\n",
    "\n",
    "- anthropic \n",
    "- voyageai\n",
    "- pandas\n",
    "- matplotlib\n",
    "- sklearn\n",
    "- numpy\n",
    "\n",
    "We will also load the API keys from the environment and set our model name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install -U anthropic voyageai pandas numpy matplotlib scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup our environment\n",
    "import os\n",
    "\n",
    "import anthropic\n",
    "\n",
    "MODEL = \"claude-haiku-4-5\"\n",
    "client = anthropic.Anthropic(\n",
    "    # This is the default and can be omitted\n",
    "    api_key=os.getenv(\"ANTHROPIC_API_KEY\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification with Claude\n",
    "\n",
    "Large Language Models (LLMs) have revolutionized the field of classification, particularly in areas where traditional machine learning systems have faced challenges. LLMs have demonstrated remarkable success in handling classification problems characterized by complex business rules and scenarios with low-quality or limited training data. Additionally, LLMs have the capability of providing natural language explanations and justifications for their actions, enhancing the interpretability and transparency of the classification process. By leveraging the power of LLMs, we can build classification systems that go beyond the capabilities of traditional machine learning approaches and excel in scenarios where data is scarce or business requirements are intricate.\n",
    "\n",
    "In this guide, we will explore how LLMs can be leveraged to tackle advanced classification tasks. We will cover the following key components and steps:\n",
    "\n",
    "1. **Data Preparation**: We will begin by preparing our training and test data. The training data will be used to build the classification model, while the test data will be utilized to assess its performance. Proper data preparation is crucial to ensure the effectiveness of our classification system.\n",
    "\n",
    "2. **Prompt Engineering**: Prompt engineering plays a vital role in harnessing the power of LLMs for classification. We will design a prompt template that defines the structure and format of the prompts used for classification. The prompt template will incorporate the user query, class definitions, and relevant examples from the vector database. By carefully crafting the prompts, we can guide the LLM to generate accurate and contextually relevant classifications.\n",
    "\n",
    "3. **Implementing Retrieval-Augmented Generation (RAG)**: To enhance the classification process, we will employ a vector database to store and efficiently retrieve embeddings of our training data. The vector database enables similarity searches, allowing us to find the most relevant examples for a given query. By augmenting the LLM with retrieved examples, we can provide additional context and improve the accuracy of the generated classifications.\n",
    "\n",
    "4. **Testing and Evaluation**: Once our classification system is built, we will rigorously test its performance using the transformed test data. We will iterate over the test queries, classify each query using the classification function, and compare the predicted categories with the expected categories. By analyzing the classification results, we can evaluate the effectiveness of our system and identify areas for improvement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Definition: Insurance Support Ticket Classifier\n",
    "\n",
    "*Note: The problem definition, data, and labels used in this example were synthetically generated by Claude 3 Opus*\n",
    "\n",
    "In the insurance industry, customer support plays a crucial role in ensuring client satisfaction and retention. Insurance companies receive a high volume of support tickets daily, covering a wide range of topics such as billing, policy administration, claims assistance, and more. Manually categorizing these tickets can be time-consuming and inefficient, leading to longer response times and potentially impacting customer experience."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Category Definitions\n",
    "\n",
    "1. Billing Inquiries\n",
    "- Questions about invoices, charges, fees, and premiums\n",
    "- Requests for clarification on billing statements\n",
    "- Inquiries about payment methods and due dates\n",
    "\n",
    "2. Policy Administration\n",
    "- Requests for policy changes, updates, or cancellations\n",
    "- Questions about policy renewals and reinstatements\n",
    "- Inquiries about adding or removing coverage options\n",
    "\n",
    "3. Claims Assistance\n",
    "- Questions about the claims process and filing procedures\n",
    "- Requests for help with submitting claim documentation\n",
    "- Inquiries about claim status and payout timelines\n",
    "\n",
    "4. Coverage Explanations\n",
    "- Questions about what is covered under specific policy types\n",
    "- Requests for clarification on coverage limits and exclusions\n",
    "- Inquiries about deductibles and out-of-pocket expenses\n",
    "\n",
    "\n",
    "5. Quotes and Proposals\n",
    "- Requests for new policy quotes and price comparisons\n",
    "- Questions about available discounts and bundling options\n",
    "- Inquiries about switching from another insurer\n",
    "\n",
    "\n",
    "6. Account Management\n",
    "- Requests for login credentials or password resets\n",
    "- Questions about online account features and functionality\n",
    "- Inquiries about updating contact or personal information\n",
    "\n",
    "\n",
    "7. Billing Disputes\n",
    "- Complaints about unexpected or incorrect charges\n",
    "- Requests for refunds or premium adjustments\n",
    "- Inquiries about late fees or collection notices\n",
    "\n",
    "\n",
    "8. Claims Disputes\n",
    "- Complaints about denied or underpaid claims\n",
    "- Requests for reconsideration of claim decisions\n",
    "- Inquiries about appealing a claim outcome\n",
    "\n",
    "\n",
    "9. Policy Comparisons\n",
    "- Questions about the differences between policy options\n",
    "- Requests for help deciding between coverage levels\n",
    "- Inquiries about how policies compare to competitors' offerings\n",
    "\n",
    "\n",
    "10. General Inquiries\n",
    "- Questions about company contact information or hours of operation\n",
    "- Requests for general information about products or services\n",
    "- Inquiries that don't fit neatly into other categories\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading and Preparing the Data\n",
    "\n",
    "Now that we've defined our problem and categories, let's load the labeled training and test data. We'll read the TSV files, convert them into a structured format, and prepare our test set for evaluation.\n",
    "\n",
    "The training data contains 68 labeled examples that we'll use for retrieval-augmented generation (RAG) later. The test set also contains 68 examples that we'll use to evaluate our classification approaches.\n",
    "\n",
    "We will use the following datasets:\n",
    "- `./data/test.tsv`\n",
    "- `./data/train.tsv`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'text': 'I just got my auto policy renewal bill and the cost seems to be more than what I usually pay. Could you explain the reason for the increase?', 'label': 'Billing Inquiries'} 68\n",
      "68 68\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = {\n",
    "    \"train\": [],\n",
    "    \"test\": [],\n",
    "}\n",
    "\n",
    "\n",
    "# Helper function to convert a DataFrame to a list of dictionaries\n",
    "def dataframe_to_dict_list(df):\n",
    "    return df.apply(lambda x: {\"text\": x[\"text\"], \"label\": x[\"label\"]}, axis=1).tolist()\n",
    "\n",
    "\n",
    "# Read the TSV file into a DataFrame\n",
    "test_df = pd.read_csv(\"./data/test.tsv\", sep=\"\\t\")\n",
    "data[\"test\"] = dataframe_to_dict_list(test_df)\n",
    "\n",
    "train_df = pd.read_csv(\"./data/train.tsv\", sep=\"\\t\")\n",
    "data[\"train\"] = dataframe_to_dict_list(train_df)\n",
    "\n",
    "\n",
    "# Understand the labels in the dataset\n",
    "labels = list(set(train_df[\"label\"].unique()))\n",
    "\n",
    "# Print the first training example and the number of training examples\n",
    "print(data[\"train\"][0], len(data[\"train\"]))\n",
    "\n",
    "# Create the test set\n",
    "X_test = [example[\"text\"] for example in data[\"test\"]]\n",
    "y_test = [example[\"label\"] for example in data[\"test\"]]\n",
    "\n",
    "# Print the length of the test set\n",
    "print(len(X_test), len(y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation Framework\n",
    "\n",
    "Before we build our classifiers, let's set up an evaluation framework to measure their performance. We'll create two key functions:\n",
    "\n",
    "1. **`evaluate(X, y, classifier, batch_size)`**: Runs your classifier on all test examples using concurrent execution for speed, then calculates accuracy metrics and generates a confusion matrix. The `batch_size` parameter controls how many concurrent API requests to make.\n",
    "\n",
    "2. **`plot_confusion_matrix(cm, labels)`**: Visualizes the confusion matrix to show which categories are being confused with each other, helping identify where the classifier struggles.\n",
    "\n",
    "This framework allows us to empirically compare different classification approaches and understand not just overall accuracy, but which specific categories are challenging.\n",
    "\n",
    "**Rate Limits**: The `MAXIMUM_CONCURRENT_REQUESTS` is set to 1 by default. If you have a higher rate limit tier, you can increase this value to speed up evaluation. See [rate limits documentation](https://docs.claude.com/en/api/rate-limits) for your tier's limits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import concurrent.futures\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "MAXIMUM_CONCURRENT_REQUESTS = 5\n",
    "\n",
    "\n",
    "def plot_confusion_matrix(cm, labels):\n",
    "    # Visualize the confusion matrix\n",
    "    fig, ax = plt.subplots(figsize=(8, 8))\n",
    "    im = ax.imshow(cm, cmap=\"Blues\")\n",
    "\n",
    "    # Add colorbar\n",
    "    ax.figure.colorbar(im, ax=ax)\n",
    "\n",
    "    # Set tick labels and positions\n",
    "    ax.set_xticks(np.arange(len(labels)))\n",
    "    ax.set_yticks(np.arange(len(labels)))\n",
    "    ax.set_xticklabels(labels, rotation=45, ha=\"right\")\n",
    "    ax.set_yticklabels(labels)\n",
    "\n",
    "    # Add labels to each cell\n",
    "    thresh = cm.max() / 2.0\n",
    "    for i in range(len(labels)):\n",
    "        for j in range(len(labels)):\n",
    "            ax.text(\n",
    "                j,\n",
    "                i,\n",
    "                cm[i, j],\n",
    "                ha=\"center\",\n",
    "                va=\"center\",\n",
    "                color=\"white\" if cm[i, j] > thresh else \"black\",\n",
    "            )\n",
    "\n",
    "    # Set labels and title\n",
    "    plt.xlabel(\"Predicted Labels\")\n",
    "    plt.ylabel(\"True Labels\")\n",
    "    plt.title(\"Confusion Matrix\")\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def evaluate(X, y, classifier, batch_size=MAXIMUM_CONCURRENT_REQUESTS):\n",
    "    # Initialize lists to store the predicted and true labels\n",
    "    y_true = []\n",
    "    y_pred = []\n",
    "\n",
    "    # Store results with their original index to maintain order\n",
    "    results = [None] * len(X)\n",
    "\n",
    "    # Create a ThreadPoolExecutor with limited workers\n",
    "    with concurrent.futures.ThreadPoolExecutor(max_workers=batch_size) as executor:\n",
    "        # Submit tasks one at a time, but executor will only run batch_size concurrently\n",
    "        future_to_index = {executor.submit(classifier, x): i for i, x in enumerate(X)}\n",
    "\n",
    "        # Process results as they complete\n",
    "        for future in concurrent.futures.as_completed(future_to_index):\n",
    "            index = future_to_index[future]\n",
    "            predicted_label = future.result()\n",
    "            results[index] = predicted_label\n",
    "\n",
    "    # Extract predictions and true labels in order\n",
    "    y_pred = results\n",
    "    y_true = y\n",
    "\n",
    "    # Normalize y_true and y_pred\n",
    "    y_true = [label.strip() for label in y_true]\n",
    "    y_pred = [label.strip() for label in y_pred]\n",
    "\n",
    "    # Calculate the classification metrics\n",
    "    report = classification_report(y_true, y_pred, labels=labels, zero_division=1)\n",
    "    cm = confusion_matrix(y_true, y_pred, labels=labels)\n",
    "    print(report)\n",
    "    plot_confusion_matrix(cm, labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baseline: Random Classifier\n",
    "\n",
    "To establish a performance baseline and verify our evaluation framework works correctly, let's start with a random classifier that picks a category at random for each ticket.\n",
    "\n",
    "This gives us a lower bound on performance: any real classification approach should significantly outperform random guessing (which should achieve roughly 10% accuracy with 10 categories). This baseline helps us quantify how much value our context engineering actually adds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "\n",
    "def random_classifier(text):\n",
    "    return random.choice(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating the random classification method on the test set...\n",
      "                       precision    recall  f1-score   support\n",
      "\n",
      "    Claims Assistance       0.14      0.17      0.15         6\n",
      " Quotes and Proposals       0.40      0.40      0.40         5\n",
      "      Claims Disputes       0.38      0.33      0.35         9\n",
      "Coverage Explanations       0.29      0.22      0.25         9\n",
      "     Billing Disputes       0.20      0.11      0.14         9\n",
      "Policy Administration       0.25      0.33      0.29         6\n",
      "    Billing Inquiries       0.00      0.00      0.00         6\n",
      "    General Inquiries       0.00      0.00      0.00         7\n",
      "   Policy Comparisons       0.00      0.00      0.00         5\n",
      "   Account Management       0.00      0.00      0.00         6\n",
      "\n",
      "             accuracy                           0.16        68\n",
      "            macro avg       0.17      0.16      0.16        68\n",
      "         weighted avg       0.18      0.16      0.17        68\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Evaluating the random classification method on the test set...\")\n",
    "evaluate(X_test, y_test, random_classifier)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected from random guessing, the confusion matrix shows predictions scattered across all categories with no meaningful pattern. The diagonal (correct predictions) shows only 1-4 correct classifications per category, while errors are distributed randomly. \n",
    "\n",
    "This confirms our baseline of approximately 10% accuracy—purely chance performance with 10 categories. Any structured classification approach should show a much stronger diagonal pattern, indicating the model is actually learning the category distinctions rather than guessing.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple Classification Test\n",
    "\n",
    "Now lets construct a simple classifier using Claude.\n",
    "\n",
    "First we will encode the categories in XML format. This will make it easier for Claude to interpret the information. Encoding information in XML is a general prompting strategy, for more information [see here](https://docs.claude.com/en/docs/build-with-claude/prompt-engineering/use-xml-tags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import textwrap\n",
    "\n",
    "categories = textwrap.dedent(\"\"\"<category>\n",
    "    <label>Billing Inquiries</label>\n",
    "    <content> Questions about invoices, charges, fees, and premiums Requests for clarification on billing statements Inquiries about payment methods and due dates\n",
    "    </content>\n",
    "</category>\n",
    "<category>\n",
    "    <label>Policy Administration</label>\n",
    "    <content> Requests for policy changes, updates, or cancellations Questions about policy renewals and reinstatements Inquiries about adding or removing coverage options\n",
    "    </content>\n",
    "</category>\n",
    "<category>\n",
    "    <label>Claims Assistance</label>\n",
    "    <content> Questions about the claims process and filing procedures Requests for help with submitting claim documentation Inquiries about claim status and payout timelines\n",
    "    </content>\n",
    "</category>\n",
    "<category>\n",
    "    <label>Coverage Explanations</label>\n",
    "    <content> Questions about what is covered under specific policy types Requests for clarification on coverage limits and exclusions Inquiries about deductibles and out-of-pocket expenses\n",
    "    </content>\n",
    "</category>\n",
    "<category>\n",
    "    <label>Quotes and Proposals</label>\n",
    "    <content> Requests for new policy quotes and price comparisons Questions about available discounts and bundling options Inquiries about switching from another insurer\n",
    "    </content>\n",
    "</category>\n",
    "<category>\n",
    "    <label>Account Management</label>\n",
    "    <content> Requests for login credentials or password resets Questions about online account features and functionality Inquiries about updating contact or personal information\n",
    "    </content>\n",
    "</category>\n",
    "<category>\n",
    "    <label>Billing Disputes</label>\n",
    "    <content> Complaints about unexpected or incorrect charges Requests for refunds or premium adjustments Inquiries about late fees or collection notices\n",
    "    </content>\n",
    "</category>\n",
    "<category>\n",
    "    <label>Claims Disputes</label>\n",
    "    <content> Complaints about denied or underpaid claims Requests for reconsideration of claim decisions Inquiries about appealing a claim outcome\n",
    "    </content>\n",
    "</category>\n",
    "<category>\n",
    "    <label>Policy Comparisons</label>\n",
    "    <content> Questions about the differences between policy options Requests for help deciding between coverage levels Inquiries about how policies compare to competitors' offerings\n",
    "    </content>\n",
    "</category>\n",
    "<category>\n",
    "    <label>General Inquiries</label>\n",
    "    <content> Questions about company contact information or hours of operation Requests for general information about products or services Inquiries that don't fit neatly into other categories\n",
    "    </content>\n",
    "</category>\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the Simple Classifier\n",
    "\n",
    "Now let's build our first real classifier using Claude. The `simple_classify` function demonstrates three key prompt engineering techniques:\n",
    "\n",
    "1. **Structured prompt template**: We provide the category definitions and the support ticket in a clear XML format, making it easy for Claude to parse the information.\n",
    "\n",
    "2. **Controlled output with prefilling**: By starting the assistant's response with `<category>` and setting `stop_sequences=[\"</category>\"]`, we force Claude to output just the category label—no explanation or extra text. This makes response parsing reliable and deterministic.\n",
    "\n",
    "3. **Deterministic classification**: Setting `temperature=0.0` ensures consistent predictions for the same input, which is critical for classification tasks.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simple_classify(X):\n",
    "    prompt = (\n",
    "        textwrap.dedent(\"\"\"\n",
    "    You will classify a customer support ticket into one of the following categories:\n",
    "    <categories>\n",
    "        {{categories}}\n",
    "    </categories>\n",
    "\n",
    "    Here is the customer support ticket:\n",
    "    <ticket>\n",
    "        {{ticket}}\n",
    "    </ticket>\n",
    "\n",
    "    Respond with just the label of the category between category tags.\n",
    "    \"\"\")\n",
    "        .replace(\"{{categories}}\", categories)\n",
    "        .replace(\"{{ticket}}\", X)\n",
    "    )\n",
    "    response = client.messages.create(\n",
    "        messages=[\n",
    "            {\"role\": \"user\", \"content\": prompt},\n",
    "            {\"role\": \"assistant\", \"content\": \"<category>\"},\n",
    "        ],\n",
    "        stop_sequences=[\"</category>\"],\n",
    "        max_tokens=4096,\n",
    "        temperature=0.0,\n",
    "        model=MODEL,\n",
    "    )\n",
    "\n",
    "    # Extract the result from the response\n",
    "    result = response.content[0].text  # pyright: ignore[reportAttributeAccessIssue]\n",
    "    return result.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating the simple classification method on the test set...\n",
      "                       precision    recall  f1-score   support\n",
      "\n",
      "    Claims Assistance       1.00      0.67      0.80         6\n",
      " Quotes and Proposals       1.00      0.60      0.75         5\n",
      "      Claims Disputes       1.00      1.00      1.00         9\n",
      "Coverage Explanations       0.53      1.00      0.69         9\n",
      "     Billing Disputes       0.67      0.67      0.67         9\n",
      "Policy Administration       0.71      0.83      0.77         6\n",
      "    Billing Inquiries       0.50      0.50      0.50         6\n",
      "    General Inquiries       1.00      0.00      0.00         7\n",
      "   Policy Comparisons       0.71      1.00      0.83         5\n",
      "   Account Management       1.00      1.00      1.00         6\n",
      "\n",
      "             accuracy                           0.74        68\n",
      "            macro avg       0.81      0.73      0.70        68\n",
      "         weighted avg       0.80      0.74      0.70        68\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Evaluating the simple classification method on the test set...\")\n",
    "evaluate(X_test, y_test, simple_classify)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much better! The confusion matrix shows a strong diagonal pattern with most categories achieving high accuracy. \n",
    "\n",
    "This ~70% overall accuracy significantly outperforms random guessing, but the confusion between similar categories suggests Claude needs more context to make finer distinctions.\n",
    "\n",
    "To address these confusion patterns, we'll add **retrieval-augmented generation (RAG)** by providing Claude with relevant examples from our training data. Here's how it works:\n",
    "\n",
    "1. **Embed training examples**: Convert all 68 training tickets into vector embeddings using VoyageAI's embedding model\n",
    "2. **Semantic search**: For each new ticket, find the 5 most similar training examples based on cosine similarity\n",
    "3. **Augment the prompt**: Include these similar examples in the classification prompt to guide Claude\n",
    "\n",
    "This approach is particularly effective for classification because:\n",
    "- Similar past examples help Claude distinguish between semantically close categories\n",
    "- Few-shot learning improves accuracy without fine-tuning\n",
    "- The retrieval is dynamic—each query gets the most relevant examples\n",
    "\n",
    "We'll build a simple `VectorDB` class to handle embedding storage and similarity search using [VoyageAI's embedding models](https://docs.claude.com/en/docs/embeddings)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "import pickle\n",
    "\n",
    "import numpy as np\n",
    "import voyageai\n",
    "\n",
    "\n",
    "class VectorDB:\n",
    "    def __init__(self, api_key=None):\n",
    "        if api_key is None:\n",
    "            api_key = os.getenv(\"VOYAGE_API_KEY\")\n",
    "        self.client = voyageai.Client(api_key=api_key)\n",
    "        self.embeddings = []\n",
    "        self.metadata = []\n",
    "        self.query_cache = {}\n",
    "        self.db_path = \"./data/vector_db.pkl\"\n",
    "\n",
    "    def load_data(self, data):\n",
    "        # Check if the vector database is already loaded\n",
    "        if self.embeddings and self.metadata:\n",
    "            print(\"Vector database is already loaded. Skipping data loading.\")\n",
    "            return\n",
    "        # Check if vector_db.pkl exists\n",
    "        if os.path.exists(self.db_path):\n",
    "            print(\"Loading vector database from disk.\")\n",
    "            self.load_db()\n",
    "            return\n",
    "\n",
    "        texts = [item[\"text\"] for item in data]\n",
    "\n",
    "        # Embed more than 128 documents with a for loop\n",
    "        batch_size = 128\n",
    "        result = [\n",
    "            self.client.embed(texts[i : i + batch_size], model=\"voyage-2\").embeddings\n",
    "            for i in range(0, len(texts), batch_size)\n",
    "        ]\n",
    "\n",
    "        # Flatten the embeddings\n",
    "        self.embeddings = [embedding for batch in result for embedding in batch]\n",
    "        self.metadata = [item for item in data]\n",
    "        self.save_db()\n",
    "        # Save the vector database to disk\n",
    "        print(\"Vector database loaded and saved.\")\n",
    "\n",
    "    def search(self, query, k=5, similarity_threshold=0.75):\n",
    "        query_embedding = None\n",
    "        if query in self.query_cache:\n",
    "            query_embedding = self.query_cache[query]\n",
    "        else:\n",
    "            query_embedding = self.client.embed([query], model=\"voyage-2\").embeddings[0]\n",
    "            self.query_cache[query] = query_embedding\n",
    "\n",
    "        if not self.embeddings:\n",
    "            raise ValueError(\"No data loaded in the vector database.\")\n",
    "\n",
    "        similarities = np.dot(self.embeddings, query_embedding)\n",
    "        top_indices = np.argsort(similarities)[::-1]\n",
    "        top_examples = []\n",
    "\n",
    "        for idx in top_indices:\n",
    "            if similarities[idx] >= similarity_threshold:\n",
    "                example = {\n",
    "                    \"metadata\": self.metadata[idx],\n",
    "                    \"similarity\": similarities[idx],\n",
    "                }\n",
    "                top_examples.append(example)\n",
    "\n",
    "                if len(top_examples) >= k:\n",
    "                    break\n",
    "        self.save_db()\n",
    "        return top_examples\n",
    "\n",
    "    def save_db(self):\n",
    "        data = {\n",
    "            \"embeddings\": self.embeddings,\n",
    "            \"metadata\": self.metadata,\n",
    "            \"query_cache\": json.dumps(self.query_cache),\n",
    "        }\n",
    "        with open(self.db_path, \"wb\") as file:\n",
    "            pickle.dump(data, file)\n",
    "\n",
    "    def load_db(self):\n",
    "        if not os.path.exists(self.db_path):\n",
    "            raise ValueError(\n",
    "                \"Vector database file not found. Use load_data to create a new database.\"\n",
    "            )\n",
    "\n",
    "        with open(self.db_path, \"rb\") as file:\n",
    "            data = pickle.load(file)\n",
    "\n",
    "        self.embeddings = data[\"embeddings\"]\n",
    "        self.metadata = data[\"metadata\"]\n",
    "        self.query_cache = json.loads(data[\"query_cache\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can define the vector db and load our training data.\n",
    "\n",
    "VoyageAI has a rate limit of 3RPM for accounts without an associated credit card. For ease of demonstration we will leverage a cache."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading vector database from disk.\n"
     ]
    }
   ],
   "source": [
    "vectordb = VectorDB()\n",
    "vectordb.load_data(data[\"train\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RAG-Enhanced Classifier\n",
    "\n",
    "Now let's rebuild our classifier with retrieval-augmented generation. The `rag_classify` function enhances the simple classifier by:\n",
    "\n",
    "1. **Retrieving similar examples**: For each ticket, we search the vector database for the 5 most semantically similar training examples\n",
    "2. **Formatting as few-shot examples**: We structure these examples in XML format with `<query>` and `<label>` tags, showing Claude how similar tickets were classified\n",
    "3. **Augmenting the prompt**: We inject these examples into the prompt before asking Claude to classify the new ticket\n",
    "\n",
    "This few-shot learning approach helps Claude make better distinctions between similar categories by showing concrete examples of how tickets with similar language should be categorized. For instance, if the test ticket mentions \"unexpected charges,\" retrieving examples of past \"Billing Disputes\" vs \"Billing Inquiries\" helps Claude understand the subtle difference.\n",
    "\n",
    "Let's see how much this improves our accuracy on the confused categories from the previous run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rag_classify(X):\n",
    "    rag = vectordb.search(X, 5)\n",
    "    rag_string = \"\"\n",
    "    for example in rag:\n",
    "        rag_string += textwrap.dedent(f\"\"\"\n",
    "        <example>\n",
    "            <query>\n",
    "                \"{example[\"metadata\"][\"text\"]}\"\n",
    "            </query>\n",
    "            <label>\n",
    "                {example[\"metadata\"][\"label\"]}\n",
    "            </label>\n",
    "        </example>\n",
    "        \"\"\")\n",
    "    prompt = (\n",
    "        textwrap.dedent(\"\"\"\n",
    "    You will classify a customer support ticket into one of the following categories:\n",
    "    <categories>\n",
    "        {{categories}}\n",
    "    </categories>\n",
    "\n",
    "    Here is the customer support ticket:\n",
    "    <ticket>\n",
    "        {{ticket}}\n",
    "    </ticket>\n",
    "\n",
    "    Use the following examples to help you classify the query:\n",
    "    <examples>\n",
    "        {{examples}}\n",
    "    </examples>\n",
    "\n",
    "    Respond with just the label of the category between category tags.\n",
    "    \"\"\")\n",
    "        .replace(\"{{categories}}\", categories)\n",
    "        .replace(\"{{ticket}}\", X)\n",
    "        .replace(\"{{examples}}\", rag_string)\n",
    "    )\n",
    "    response = client.messages.create(\n",
    "        messages=[\n",
    "            {\"role\": \"user\", \"content\": prompt},\n",
    "            {\"role\": \"assistant\", \"content\": \"<category>\"},\n",
    "        ],\n",
    "        stop_sequences=[\"</category>\"],\n",
    "        max_tokens=4096,\n",
    "        temperature=0.0,\n",
    "        model=\"claude-haiku-4-5\",\n",
    "    )\n",
    "\n",
    "    # Extract the result from the response\n",
    "    result = response.content[0].text.strip()\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating the RAG method on the test set...\n",
      "                       precision    recall  f1-score   support\n",
      "\n",
      "    Claims Assistance       1.00      1.00      1.00         6\n",
      " Quotes and Proposals       1.00      1.00      1.00         5\n",
      "      Claims Disputes       1.00      1.00      1.00         9\n",
      "Coverage Explanations       0.82      1.00      0.90         9\n",
      "     Billing Disputes       0.82      1.00      0.90         9\n",
      "Policy Administration       1.00      1.00      1.00         6\n",
      "    Billing Inquiries       1.00      0.67      0.80         6\n",
      "    General Inquiries       1.00      0.71      0.83         7\n",
      "   Policy Comparisons       1.00      1.00      1.00         5\n",
      "   Account Management       1.00      1.00      1.00         6\n",
      "\n",
      "             accuracy                           0.94        68\n",
      "            macro avg       0.96      0.94      0.94        68\n",
      "         weighted avg       0.95      0.94      0.94        68\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Evaluating the RAG method on the test set...\")\n",
    "evaluate(X_test, y_test, rag_classify)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RAG Results Analysis\n",
    "\n",
    "RAG boosted our accuracy from ~70% to **94%**. The confusion matrix shows a much stronger diagonal, with most categories now achieving perfect or near-perfect accuracy.\n",
    "\n",
    "The few remaining errors suggest that even with relevant examples, some edge cases remain ambiguous. This is where chain-of-thought reasoning can help Claude think through the subtle distinctions more carefully."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RAG with Chain-of-Thought Reasoning\n",
    "\n",
    "Chain-of-thought (CoT) prompting asks Claude to \"think out loud\" before making a classification decision. \n",
    "\n",
    "This explicit reasoning process helps Claude catch subtle distinctions that might be missed with direct classification. For example, distinguishing \"I have a question about my bill\" (Billing Inquiry) from \"This charge seems wrong\" (Billing Dispute) requires understanding intent and tone—something that benefits from step-by-step analysis.\n",
    "\n",
    "The `rag_chain_of_thought_classify` function adds a `<scratchpad>` section where Claude works through this reasoning before outputting the final `<category>`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rag_chain_of_thought_classify(X):\n",
    "    rag = vectordb.search(X, 5)\n",
    "    rag_string = \"\"\n",
    "    for example in rag:\n",
    "        rag_string += textwrap.dedent(f\"\"\"\n",
    "        <example>\n",
    "            <query>\n",
    "                \"{example[\"metadata\"][\"text\"]}\"\n",
    "            </query>\n",
    "            <label>\n",
    "                {example[\"metadata\"][\"label\"]}\n",
    "            </label>\n",
    "        </example>\n",
    "        \"\"\")\n",
    "    prompt = (\n",
    "        textwrap.dedent(\"\"\"\n",
    "    You will classify a customer support ticket into one of the following categories:\n",
    "    <categories>\n",
    "        {{categories}}\n",
    "    </categories>\n",
    "\n",
    "    Here is the customer support ticket:\n",
    "    <ticket>\n",
    "        {{ticket}}\n",
    "    </ticket>\n",
    "\n",
    "    Use the following examples to help you classify the query:\n",
    "    <examples>\n",
    "        {{examples}}\n",
    "    </examples>\n",
    "\n",
    "    First you will think step-by-step about the problem in scratchpad tags.\n",
    "    You should consider all the information provided and create a concrete argument for your classification.\n",
    "\n",
    "    Respond using this format:\n",
    "    <response>\n",
    "        <scratchpad>Your thoughts and analysis go here</scratchpad>\n",
    "        <category>The category label you chose goes here</category>\n",
    "    </response>\n",
    "    \"\"\")\n",
    "        .replace(\"{{categories}}\", categories)\n",
    "        .replace(\"{{ticket}}\", X)\n",
    "        .replace(\"{{examples}}\", rag_string)\n",
    "    )\n",
    "    response = client.messages.create(\n",
    "        messages=[\n",
    "            {\"role\": \"user\", \"content\": prompt},\n",
    "            {\"role\": \"assistant\", \"content\": \"<response><scratchpad>\"},\n",
    "        ],\n",
    "        stop_sequences=[\"</category>\"],\n",
    "        max_tokens=4096,\n",
    "        temperature=0.0,\n",
    "        model=MODEL,\n",
    "    )\n",
    "\n",
    "    # Extract the result from the response\n",
    "    result = response.content[0].text.split(\"<category>\")[1].strip()\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating the RAG method with Chain of Thought on the test set...\n",
      "                       precision    recall  f1-score   support\n",
      "\n",
      "    Claims Assistance       1.00      1.00      1.00         6\n",
      " Quotes and Proposals       1.00      1.00      1.00         5\n",
      "      Claims Disputes       1.00      1.00      1.00         9\n",
      "Coverage Explanations       1.00      1.00      1.00         9\n",
      "     Billing Disputes       0.82      1.00      0.90         9\n",
      "Policy Administration       1.00      1.00      1.00         6\n",
      "    Billing Inquiries       1.00      0.67      0.80         6\n",
      "    General Inquiries       1.00      1.00      1.00         7\n",
      "   Policy Comparisons       1.00      1.00      1.00         5\n",
      "   Account Management       1.00      1.00      1.00         6\n",
      "\n",
      "             accuracy                           0.97        68\n",
      "            macro avg       0.98      0.97      0.97        68\n",
      "         weighted avg       0.98      0.97      0.97        68\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Evaluating the RAG method with Chain of Thought on the test set...\")\n",
    "evaluate(X_test, y_test, rag_chain_of_thought_classify)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chain-of-Thought Results Analysis\n",
    "\n",
    "Chain-of-thought reasoning pushed our accuracy to **97%**, resolving most of the edge cases that challenged the previous approaches.\n",
    "\n",
    "**Progressive improvement summary:**\n",
    "- Random baseline: ~10% accuracy\n",
    "- Simple classifier: ~70% accuracy\n",
    "- RAG classifier: 94% accuracy  \n",
    "- **RAG + Chain-of-Thought: 97% accuracy**\n",
    "\n",
    "By explicitly reasoning through each classification decision, Claude better distinguishes between ambiguous cases. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scaling Evaluation with Promptfoo\n",
    "\n",
    "Throughout this guide, we've demonstrated the importance of **empirical evaluation** when engineering prompts. Rather than relying on intuition, we measured performance at each step:\n",
    "\n",
    "- Random baseline: ~10% → Simple: ~70% → RAG: 94% → RAG + CoT: 97%\n",
    "\n",
    "This data-driven approach revealed exactly which techniques (RAG, chain-of-thought) added value and by how much. For more on this methodology, see our [prompt engineering guide](https://docs.claude.com/en/docs/prompt-engineering).\n",
    "\n",
    "## Moving Beyond Notebooks\n",
    "\n",
    "While Jupyter notebooks are excellent for rapid iteration and exploration, production classification systems require more robust evaluation infrastructure:\n",
    "\n",
    "- **Larger test sets**: You'll want to evaluate on hundreds or thousands of examples, not just 68\n",
    "- **Multiple prompt variants**: A/B testing different phrasings, structures, and approaches\n",
    "- **Model comparisons**: Testing across different Claude models (Haiku vs Sonnet) or temperature settings\n",
    "- **Regression detection**: Ensuring prompt changes don't accidentally hurt accuracy\n",
    "- **Version control**: Tracking prompt performance over time as your system evolves\n",
    "\n",
    "This is where dedicated evaluation tools like [Promptfoo](https://www.promptfoo.dev/) become essential.\n",
    "\n",
    "## Running Promptfoo Evaluation\n",
    "\n",
    "Promptfoo is an open-source LLM evaluation toolkit that automates prompt testing across multiple configurations. For this guide, we've set up a Promptfoo evaluation that tests all three approaches (Simple, RAG, RAG w/ CoT) across different temperature settings.\n",
    "\n",
    "**To run the evaluation:**\n",
    "\n",
    "1. Navigate to the evaluation directory: `cd ./evaluation`\n",
    "2. Follow the setup instructions in `./evaluation/README.md`\n",
    "3. Run the evaluation and return here to analyze the results below\n",
    "\n",
    "The results will show how each prompt variant performs across the full test set, revealing patterns that might not be obvious from notebook testing alone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Haiku: T-0.0 Prompt: RAG w/ CoT    95.588235\n",
      "Haiku: T-0.2 Prompt: RAG w/ CoT    95.588235\n",
      "Haiku: T-0.8 Prompt: RAG w/ CoT    95.588235\n",
      "Haiku: T-0.0 Prompt: RAG           94.117647\n",
      "Haiku: T-0.4 Prompt: RAG           94.117647\n",
      "Haiku: T-0.6 Prompt: RAG           94.117647\n",
      "Haiku: T-0.4 Prompt: RAG w/ CoT    94.117647\n",
      "Haiku: T-0.6 Prompt: RAG w/ CoT    94.117647\n",
      "Haiku: T-0.2 Prompt: RAG           92.647059\n",
      "Haiku: T-0.8 Prompt: RAG           89.705882\n",
      "Haiku: T-0.6 Prompt: Simple        72.058824\n",
      "Haiku: T-0.0 Prompt: Simple        70.588235\n",
      "Haiku: T-0.2 Prompt: Simple        70.588235\n",
      "Haiku: T-0.8 Prompt: Simple        70.588235\n",
      "Haiku: T-0.4 Prompt: Simple        69.117647\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "promptfoo_results = pd.read_csv(\"./data/results.csv\")\n",
    "examples_columns = promptfoo_results.columns[2:]\n",
    "\n",
    "number_of_providers = 5\n",
    "number_of_prompts = 3\n",
    "\n",
    "prompts = [\"Simple\", \"RAG\", \"RAG w/ CoT\"]\n",
    "\n",
    "columns = [\"label\", \"text\"] + [\n",
    "    json.loads(examples_columns[prompt * number_of_providers + provider])[\"provider\"]\n",
    "    + \" Prompt: \"\n",
    "    + str(prompts[prompt])\n",
    "    for prompt in range(number_of_prompts)\n",
    "    for provider in range(number_of_providers)\n",
    "]\n",
    "\n",
    "promptfoo_results.columns = columns\n",
    "\n",
    "result = (\n",
    "    promptfoo_results.iloc[:, 2:].astype(str).apply(lambda x: x.str.count(\"PASS\")).sum()\n",
    "    / len(promptfoo_results)\n",
    "    * 100\n",
    ").sort_values(ascending=False)\n",
    "\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Systematic Evaluation with Promptfoo\n",
    "\n",
    "The results above demonstrate the power of systematic prompt evaluation at scale. While our notebook provided quick iteration during development, [Promptfoo](https://www.promptfoo.dev/) enabled us to rigorously test our prompts across multiple dimensions:\n",
    "\n",
    "### What We Tested\n",
    "\n",
    "Using Promptfoo, we evaluated all three prompt approaches (Simple, RAG, RAG w/ CoT) across:\n",
    "- **Multiple temperature settings** (0.0, 0.2, 0.4, 0.6, 0.8): Testing whether deterministic classification (T=0.0) outperforms slightly randomized outputs\n",
    "- **The same test set** (68 examples): Ensuring fair comparison across all configurations\n",
    "- **Automated pass/fail checking**: Each prediction is automatically compared against ground truth labels\n",
    "\n",
    "### Key Findings\n",
    "\n",
    "The Promptfoo results confirm our notebook findings while revealing additional insights:\n",
    "\n",
    "1. **Temperature has minimal impact on CoT**: RAG w/ CoT achieves 95.59% accuracy consistently across T=0.0, 0.2, and 0.8, suggesting the chain-of-thought reasoning stabilizes outputs regardless of sampling randomness\n",
    "\n",
    "2. **RAG is robust to temperature variation**: RAG performance stays strong (89-94%) across most temperature settings, though T=0.0 performs best (94.12%)\n",
    "\n",
    "3. **Simple prompts are temperature-agnostic**: The simple classifier hovers around 70% accuracy regardless of temperature, confirming that without RAG or CoT, the model relies primarily on category definitions\n",
    "\n",
    "4. **Production recommendation**: Use `temperature=0.0` with RAG w/ CoT for maximum consistency and accuracy (95.59%)\n",
    "\n",
    "To run these evaluations yourself, see `./evaluation/README.md` for setup instructions."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "anthropic-cookbook (3.12.12)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
